While references to external definitions are URIs, it is strongly recommended that CD files be retrievable at the location obtained by interpreting the URI as a URL. In particular, other properties of the symbol being defined may be available by inspecting the Content Dictionary specified. These include not only the symbol definition, but also examples and other formal properties. Note, however, that there are multiple encodings for OpenMath Content Dictionaries, and it is up to the user agent to correctly determine the encoding when retrieving a CD.
Unfortunately, no high-confidence peptides overlapped diagnostic amino acid positions with sequence differences between H. sapiens, Denisovans, or Neanderthals, making further taxonomic assignment based on palaeoproteomics impossible. This is in line with previous research, which indicated that closely related hominin populations can be distinguished based on dentine and bone proteomes, while enamel proteomes are less informative in the context of close phylogenetic proximity35. Nevertheless, by comparing the sequences recovered from the TNH2-1 enamel proteome with that of extant hominids for which protein sequences are available, we find that the specimen belongs to a member of the genus Homo (Supplementary Table 8).
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The tests revealed that the flattest plateau was provided by the pIR-IRSL50,270, pIR-IRSL50,290 and pIR-IRSL200,290 signals (Supplementary Fig. 10c), while the pIR-IRSL50,270 signal provided the best recovery of the surrogate dose (with a dose recovery ratio of 0.995) and lowest residual values after bleaching (
Xu et al. [19] proposed an algorithm for detecting data verification results to resist counterfeit fraud attacks from untrusted verification results. The algorithm performs cross-validation by establishing a dual evidence mode of integrity verification proof and incredible check proof. Integrity verification proof is used to check the integrity of the data, and incredible check proof is used to determine the correctness of the data verification results, but the introduction of secondary verification evidence to cross-check verification results increases the computation and storage overhead. Shen et al. [20] proposed a new data integrity verification scheme that enables files in cloud storage to be shared securely without affecting privacy and integrity verification. Li et al. [21] proposed a provable data integrity method, which improves the verification efficiency by reducing the user cost during the initialization phase. Zhu et al. [22] proposed an integrity verification scheme based on a short signature algorithm (ZSS signature) for the IoT environment, which proved to be secure and efficient.
The user has the ownership of the data files and the local storage space is limited, so user chooses to entrust the files to the CSP. For the sake of cloud data security, the user will check the integrity of the uploaded data from time to time.
The proposed scheme uses the lattice signature algorithm to sign the files on the user side, the cuckoo filter is also used to simplify the user verification process, and the blockchain network is introduced to record the interaction between the user and the CSP. The scheme mainly includes six parts: KeyGen(), SigGen(), Upload(), Challenge(), ProofGen(), and Verify(). The process is shown in Figure 4 and the details are given below.
Generally speaking, files are not immutable after being uploaded by users. In practical applications, users will need to update files, such as add, delete, and modify. So, we use MHT to support dynamic operation. Simultaneously, the proposed scheme reduces the complexity of the verification process by introducing cuckoo filter, so the dynamic operation of the scheme involves two parts. The first part is the update operation of MHT, and the second part is the update operation of the cuckoo filter.
As for the communication overhead, during the challenge phase, the audit request contains only numbers representing the file number to be validated, so the communication cost in this phase is negligible. During the GenProof phase, the CSP needs to return the signatures and the relevant files requested by the user, so the communication cost is , where represents the number of blocks required in the challenge phase, represents the signature length of each file, and represents the size of the file block.
You can convert selective emails or a complete folder of EML to PST (Personal Storage Table) format with the folder hierarchy upheld. It will maintain the email formatting and export attachments as embedded. For batch conversion browse complete folder at once and software will convert all EML files to PST format in single click.
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The EML to PST Converter provides an option to convert selected EML files to PST file format. To activate this option you have to check the check-box besides Date Filter Option and provide the date range. The software will include only those emails, which will lie in between the provided date range.
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This manual describes NCO, which stands for netCDF Operators.NCO is a suite of programs known as operators.Each operator is a standalone, command line program executed at theshell-level like, e.g., ls or mkdir. The operators take netCDF files (including HDF5 filesconstructed using the netCDF API) as input, perform anoperation (e.g., averaging or hyperslabbing), and produce a netCDF file as output. The operators are primarily designed to aid manipulation and analysis of data.The examples in this documentation are typical applications of theoperators for processing climate model output. This stems from their origin, though the operators are as general asnetCDF itself.
The documentation for NCO is called the NCO User Guide. The User Guide is available in PDF, Postscript,HTML, DVI, TeXinfo, and Info formats.These formats are included in the source distribution in the filesnco.pdf, nco.ps, nco.html, nco.dvi,nco.texi, and nco.info*, respectively.All the documentation descends from a single source file,nco.texi1.Hence the documentation in every format is very similar.However, some of the complex mathematical expressions needed to describencwa can only be displayed in DVI, Postscript, and PDF formats.
However, the ability to compile NCO with only netCDF2calls is worth maintaining because HDF version 4, aka HDF4 or simply HDF, 6 (available from HDF)supports only the netCDF2 library calls(see _html/SDS_SD.fm12.html#47784).There are two versions of HDF.Currently HDF version 4.x supports the full netCDF2API and thus NCO version 1.2.x. If NCO version 1.2.x (or earlier) is built with onlynetCDF2 calls then all NCO operators should work with HDF4 files as well as netCDF files7.The preprocessor token NETCDF2_ONLY existsin NCO version 1.2.x to eliminate all netCDF3calls. Only versions of NCO numbered 1.2.x and earlier have thiscapability.
When linked to a netCDF library that was built with HDF4support8,NCO automatically supports reading HDF4 files and writing them as netCDF3/netCDF4/HDF5 files.NCO can only write through the netCDF API, whichcan only write netCDF3/netCDF4/HDF5 files. So NCO can read HDF4 files, performmanipulations and calculations, and then it must write theresults in netCDF format.
Finally, in February 2014, we learned that the HDF grouphas a project called H4CF (described here)whose goal is to make HDF4 files accessible to CFtools and conventions. Their project includes a tool named h4tonccf that convertsHDF4 files to netCDF3 or netCDF4 files.We are not yet sure what advantages or features h4tonccf hasthat are not in NCO, though we suspect both methods have theirown advantages. Corrections welcome.
The main design goal is command line operators which perform useful,scriptable operations on netCDF files. Many scientists work with models and observations which produce too muchdata to analyze in tabular format.Thus, it is often natural to reduce and massage this raw or primarylevel data into summary, or second level data, e.g., temporal or spatialaverages. These second level data may become the inputs to graphical andstatistical packages, and are often more suitable for archival anddissemination to the scientific community.NCO performs a suite of operations useful in manipulating datafrom the primary to the second level state.Higher level interpretive languages (e.g., IDL, Yorick,Matlab, NCL, Perl, Python),and lower level compiled languages (e.g., C, Fortran) can always perform any task performed by NCO, but often with more overhead.NCO, on the other hand, is limited to a much smaller set of arithmeticand metadata operations than these full blown languages. 2ff7e9595c
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